first commit

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

README.md

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----
-title: Nlp1
-emoji: 🐢
-colorFrom: pink
-colorTo: red
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Get your questions Answered (Build Question Answering System)
+Input: Context and Question
+Output: Answer

code/qa.py

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+import streamlit as st
+from transformers import pipeline
+
+def load_file():
+    """Load text from file"""
+    uploaded_file = st.file_uploader("Upload Files",type=['txt'])
+
+    if uploaded_file is not None:
+        if uploaded_file.type == "text/plain":
+            raw_text = str(uploaded_file.read(),"utf-8")
+        return raw_text
+
+
+if __name__ == "__main__":
+
+    # App title and description
+    st.title("Answering questions from text")
+    st.write("Upload text, pose questions, get answers")
+
+    # Load file
+    raw_text = load_file()
+    if raw_text != None and raw_text != '':
+
+        # Display text
+        with st.expander("See text"):
+            st.write(raw_text)
+
+        # Perform question answering
+        question_answerer = pipeline('question-answering')
+
+        answer = ''
+        question = st.text_input('Ask a question')
+
+        if question != '' and raw_text != '':
+            answer = question_answerer({
+                'question': question,
+                'context': raw_text
+            })
+
+        st.write(answer["answer"])
+        st.write("Confidence score: {}".format(answer["score"]))
+        

input/input1.txt

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+Talk to your stakeholders before building any data product. Understand the business’ needs at the current stage: If it’s a startup, I bet your stakeholder won’t care too much about the format and color of the data visualizations you build but wants to instead focus on the accuracy of the data behind the visualizations and insights from them. Similarly, truly understand the audience and use case; for example, you would spend more time on a polished and simple user interface if the data product is intended to be used regularly by non-technical audiences.